[1] NeuralFactors神经因子来源:ARXIV_20240806[2] Machine Learning based Relative Valuation of Municipal Bonds基于机器学习的市政债券相对估值来源:ARXIV_20240806[3] Data time travel and consistent market making数据时间旅行和一致的做市来源:ARXIV_20240806[4] An Integrated Approach to Importance Sampling and Machine Learning for Efficient Monte Carlo Estimation of Distortion Risk Measures in Black Box Models重要抽样和机器学习的集成方法，用于黑盒模型中失真风险度量的有效蒙特卡洛估计来源:ARXIV_20240806[5] Climate Driven Doubling of Maize Loss Probability in U.S. Crop Insurance美国作物保险中气候驱动的玉米损失概率翻倍来源:ARXIV_20240806[6] Hedge Fund Portfolio Construction Using PolyModel Theory and iTransformer基于多元模型理论和iTransformer的对冲基金投资组合构建来源:ARXIV_20240807[7] KAN based Autoencoders for Factor Models基于KAN的因子模型自动编码器来源:ARXIV_20240807
[1] NeuralFactors
标题:神经因子作者:Achintya Gopal来源:ARXIV_20240806Abstract : The use of machine learning for statistical modeling (and thus, generative modeling) has grown in popularity with the proliferation of time series models, text to image models, and especially large language models. Fundamentally, the goal of classical factor modeling is statistical modeling of stock returns, and in this work, we explore using deep generative modeling to enhance classical factor models. Prior......(摘要翻译及全文见知识星球)Keywords :
[2] Machine Learning based Relative Valuation of Municipal Bonds
标题:基于机器学习的市政债券相对估值作者:Preetha Saha, Jingrao Lyu, Dhruv Desai, Rishab Chauhan, Jerinsh Jeyapaulraj, Philip Sommer, Dhagash Mehta来源:ARXIV_20240806Abstract : The trading ecosystem of the Municipal (muni) bond is complex and unique. With nearly 2 of securities from over a million securities outstanding trading daily, determining the value or relative value of a bond among its peers is challenging. Traditionally, relative value calculation has been done using rule based or heuristics driven approaches, which may introduce human biases and......(摘要翻译及全文见知识星球)Keywords :
[3] Data time travel and consistent market making
标题:数据时间旅行和一致的做市作者:Vincent Ragel, Damien Challet来源:ARXIV_20240806
Abstract : Reinforcement learning works best when the impact of the agent s actions on its environment can be perfectly simulated or fully appraised from available data. Some systems are however both hard to simulate and very sensitive to small perturbations. An additional difficulty arises when an RL agent must learn to be part of a multi agent system using only anonymous data,......(摘要翻译及全文见知识星球)Keywords :
[4] An Integrated Approach to Importance Sampling and Machine Learning for Efficient Monte Carlo Estimation of Distortion Risk Measures in Black Box Models
标题:重要抽样和机器学习的集成方法，用于黑盒模型中失真风险度量的有效蒙特卡洛估计作者:Sören Bettels, Stefan Weber来源:ARXIV_20240806Abstract : Distortion risk measures play a critical role in quantifying risks associated with uncertain outcomes. Accurately estimating these risk measures in the context of computationally expensive simulation models that lack analytical tractability is fundamental to effective risk management and decision making. In this paper, we propose an efficient important sampling method for distortion risk measures in such models that reduces the computational......(摘要翻译及全文见知识星球)Keywords :
[5] Climate Driven Doubling of Maize Loss Probability in U.S. Crop Insurance
标题:美国作物保险中气候驱动的玉米损失概率翻倍作者:A Samuel Pottinger, Lawson Connor, Brookie Guzder-Williams, Maya Weltman-Fahs, Timothy Bowles来源:ARXIV_20240806Abstract : Climate change not only threatens agricultural producers but also strains financial institutions. These important food system actors include government entities tasked with both insuring grower livelihoods and supporting response to continued global warming. We use an artificial neural network to predict future maize yields in the U.S. Corn Belt, finding alarming changes to institutional risk exposure within the Federal Crop Insurance......(摘要翻译及全文见知识星球)Keywords :
[6] Hedge Fund Portfolio Construction Using PolyModel Theory and iTransformer
标题:基于多元模型理论和iTransformer的对冲基金投资组合构建作者:Siqiao Zhao, Zhikang Dong, Zeyu Cao, Raphael Douady来源:ARXIV_20240807Abstract : When constructing portfolios, a key problem is that a lot of financial time series data are sparse, making it challenging to apply machine learning methods. Polymodel theory can solve this issue and demonstrate superiority in portfolio construction from various aspects. To implement the PolyModel theory for constructing a hedge fund portfolio, we begin by identifying an asset pool, utilizing over 10,000......(摘要翻译及全文见知识星球)Keywords :
[7] KAN based Autoencoders for Factor Models
标题:基于KAN的因子模型自动编码器作者:Tianqi Wang, Shubham Singh来源:ARXIV_20240807Abstract : Inspired by recent advances in Kolmogorov Arnold Networks (KANs), we introduce a novel approach to latent factor conditional asset pricing models. While previous machine learning applications in asset pricing have predominantly used Multilayer Perceptrons with ReLU activation functions to model latent factor exposures, our method introduces a KAN based autoencoder which surpasses MLP models in both accuracy and interpretability. Our model......(摘要翻译及全文见知识星球)Keywords :
